Implications of Model Uncertainty for Bank Stress Testing

Abstract

We aim to raise the awareness that model uncertainty stemming from stress test satellite equations that relate bank risk parameters to macro-financial variables can be significant. Based on a set of credit risk models derived by means of a Bayesian model averaging (BMA) methodology we conduct a stress test for 75 European banks to highlight that i) an optimistic equation choice can imply significantly overstated capital estimates, ii) model uncertainty contributes on average about 35% to overall uncertainty in our application, and iii) the impact of model uncertainty feeding through regulatory risk weights can easily turn twice as sizable as that from loan losses. Model methods that account for model uncertainty, such as the BMA, should mitigate the risks arising along these three dimensions and help establish a level playing field with regard to an equal extent of conservatism across banks.

Keywords

Stress testing Model uncertainty Bank regulation and supervision

JEL Classification

Appendix

Table 3

List of banks included in the assessment. The 75 banks listed in this table represent the most significant banking groups from a subset of 10 European countries that the Single Supervisory Mechanism (SSM) comprises. The 10 countries correspond to those for which models are developed in this paper. SSM banks from Cyprus, Luxembourg, Latvia, Malta and Slovenia were excluded because no models were developed (due to insufficient quality/time series coverage for corporate PDs in these cases)